Clinical data is a crucial asset in modern healthcare. From electronic health records (EHRs) captured during routine check-ups to the real-time monitoring of chronic conditions, the healthcare system generates a vast and complex repository of information every day. This data holds the potential to revolutionize patient care by providing healthcare providers with the insights they need to make informed decisions and deliver personalized treatment plans.
The true power of this data is unlocked when it is accurate and complete. When presented effectively, it enables healthcare professionals to take practical steps or make informed decisions. This is where healthcare faces one of its most significant challenges: achieving meaningful interoperability. As healthcare organizations seek to integrate data from multiple sources, the issues of data quality, consistency, and accessibility become increasingly apparent. Addressing these challenges is essential to realizing the promise of data-driven healthcare.
Healthcare’s clinical data dilemma stems from its complexity. Systems and departments disperse information, each with its own standards and formats. Variations in coding practices, documentation errors, and duplicate entries further complicate the integration of clinical data into a cohesive whole. This fragmentation not only hampers the efficiency of healthcare operations but also obscures critical insights that could improve patient outcomes.
The chart below outlines four barriers to data usability:
At Availity, our clinical informatics team conducts data quality analyses as part of our deployments, including annual real-world data audits. We routinely find that upwards of 50% of source clinical data cannot be used in its raw native form because of its incompleteness, lack of interpretability or absence of medical concepts. We believe that the industry must commit to both data interoperability standards and applying technology to fix data quality in order to ensure the safety and efficacy of healthcare delivery.
For healthcare providers, the consequences of poor data quality are profound. Inaccurate or incomplete information can lead to misdiagnoses, inappropriate treatments, and missed opportunities to close care gaps. Moreover, the time and effort required to manually reconcile disparate data sources can burden healthcare teams. This diverts resources away from patient care. To tackle these challenges, healthcare organizations need to implement advanced interoperability solutions that go beyond merely connecting disparate data sources. They must also transform and enrich clinical data to ensure its meaningful use.
Our latest case study reveals how a leading data interoperability organization, renowned for delivering concise, one-page patient summaries for primary and virtual specialty care providers, joined forces with Availity. By leveraging Availity Fusion™, an advanced automated data transformation engine, the organization achieved remarkable improvements in clinical data quality. Dive into the case study to discover how this partnership created streamlined patient summaries. Providers gained access to recent medical updates and customized clinical content, ultimately enhancing care management. To read the full case study, click here.
Praveer Mathur is an accomplished leader with over 20 years of experience in the healthcare industry, encompassing biomedical research, clinical workflow application development, data interoperability, and advanced analytics. He holds a Master of Science from the University of Connecticut and a Certificate in Leadership Strategies for IT in Healthcare from Harvard University.
Praveer’s vision in healthcare centers on minimizing costs, improving outcomes, and streamlining experiences. He dedicates himself to achieving these goals through process simplification and technological innovation, ensuring that healthcare becomes more accessible and efficient for all stakeholders.
Praveer Mathur
Director of Product, Connectivity & Interoperability at Availity